The video examines a major cybersecurity incident involving CrowdStrike, where a coding error led to a critical failure in their Windows Defender plugin, impacting billions of computers. It explores the potential of the open-source AI model Deepseek Coder to diagnose and fix such coding issues, demonstrating its effectiveness in recognizing errors and highlighting the growing role of AI in software development.
The video discusses a significant cybersecurity incident involving CrowdStrike, which led to a massive failure in their Windows Defender plugin, affecting billions of computers worldwide. The root cause was identified as a coding error that resulted in a buffer overflow, an elementary mistake that managed to bypass the scrutiny of professional engineers at CrowdStrike. The incident has been labeled as one of the most costly in cybersecurity history, with only specific groups, such as those using outdated systems or alternative operating systems, being unaffected.
A Twitter discussion emerged about whether an open-source large language model (LLM), specifically Deepseek Coder, could have identified and prevented the coding error that led to this incident. The video presents an experiment where Deepseek Coder was tasked with diagnosing the issue and determining how to fix the problems caused by CrowdStrike’s update. Although it didn’t provide a perfect solution on the first attempt, it demonstrated the potential of LLMs in recognizing C coding errors when provided with sufficient context.
The presenter highlights that Deepseek Coder is a powerful coding model that has been fine-tuned for optimal performance on CPUs, making it accessible for users without high-end GPUs. The model can be stored on a modest-sized flash drive, allowing for easy distribution and storage. The video emphasizes the emergence of local co-pilot tools that provide similar code assistance as commercial services like GitHub Copilot, but at no cost, marking a shift in the coding assistance landscape.
The initial tests conducted with Deepseek Coder involved prompts that simulated a scenario where a developer needed to troubleshoot the CrowdStrike update issue. The model successfully provided troubleshooting steps but required further context to refine its suggestions. In the end, the model’s ability to generate insights was impressive, showcasing the potential for AI tools in enhancing software engineering and problem-solving capabilities.
The video concludes with a discussion on the importance of crafting effective system prompts to harness the full potential of LLMs like Deepseek Coder. The presenter invites viewers to share their experiences with using LLMs for troubleshooting and encourages interaction on the topic. Overall, the content underscores the growing relevance of AI in software development and the potential for such models to assist engineers in resolving complex issues more efficiently.